Removed ngraph mentions (#10647)
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@ -843,16 +843,6 @@ INPUT = "@MARKDOWN_INPUT@" \
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"@OpenVINO_SOURCE_DIR@/src/common/transformations/include/" \
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"@OpenVINO_SOURCE_DIR@/src/common/util/include/" \
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"@OpenVINO_SOURCE_DIR@/src/core/include/" \
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"@OpenVINO_SOURCE_DIR@/src/core/include/ngraph/" \
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"@OpenVINO_SOURCE_DIR@/src/core/include/ngraph/descriptor" \
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"@OpenVINO_SOURCE_DIR@/src/core/include/ngraph/op/" \
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"@OpenVINO_SOURCE_DIR@/src/core/include/ngraph/op/util" \
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"@OpenVINO_SOURCE_DIR@/src/core/include/ngraph/opsets/" \
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"@OpenVINO_SOURCE_DIR@/src/core/include/ngraph/pass/" \
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"@OpenVINO_SOURCE_DIR@/src/core/include/ngraph/pattern/" \
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"@OpenVINO_SOURCE_DIR@/src/core/include/ngraph/pattern/op/" \
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"@OpenVINO_SOURCE_DIR@/src/core/include/ngraph/runtime/" \
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"@OpenVINO_SOURCE_DIR@/src/core/include/ngraph/type/" \
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"@OpenVINO_SOURCE_DIR@/src/core/include/openvino/" \
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"@OpenVINO_SOURCE_DIR@/src/core/include/openvino/core/" \
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"@OpenVINO_SOURCE_DIR@/src/core/include/openvino/core/descriptor/" \
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@ -91,7 +91,7 @@ This section provides reference documents that guide you through developing your
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With the [Model Downloader](@ref omz_tools_downloader) and [Model Optimizer](MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) guides, you will learn to download pre-trained models and convert them for use with the OpenVINO™ toolkit. You can provide your own model or choose a public or Intel model from a broad selection provided in the [Open Model Zoo](model_zoo.md).
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## Deploying Inference
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The [OpenVINO™ Runtime User Guide](OV_Runtime_UG/openvino_intro.md) explains the process of creating your own application that runs inference with the OpenVINO™ toolkit. The [API Reference](./api_references.html) defines the Inference Engine API for Python, C++, and C and the nGraph API for Python and C++. The Inference Engine API is what you'll use to create an OpenVINO™ application, while the nGraph API is available for using enhanced operations sets and other features. After writing your application, you can use the [Deployment Manager](install_guides/deployment-manager-tool.md) for deploying to target devices.
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The [OpenVINO™ Runtime User Guide](OV_Runtime_UG/openvino_intro.md) explains the process of creating your own application that runs inference with the OpenVINO™ toolkit. The [API Reference](./api_references.html) defines the OpenVINO Runtime API for Python, C++, and C. The OpenVINO Runtime API is what you'll use to create an OpenVINO™ inference application, use enhanced operations sets and other features. After writing your application, you can use the [Deployment Manager](install_guides/deployment-manager-tool.md) for deploying to target devices.
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## Tuning for Performance
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The toolkit provides a [Performance Optimization Guide](optimization_guide/dldt_optimization_guide.md) and utilities for squeezing the best performance out of your application, including [Accuracy Checker](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool](@ref pot_README), and other tools for measuring accuracy, benchmarking performance, and tuning your application.
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@ -3,7 +3,7 @@
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This specification document describes `opset1` operation set supported in OpenVINO.
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Support for each particular operation from the list below depends on the capabilities available in a inference plugin
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and may vary among different hardware platforms and devices. Examples of operation instances are expressed as IR V10 xml
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snippets. Such IR is generated by the Model Optimizer. The semantics match corresponding nGraph operation classes
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snippets. Such IR is generated by the Model Optimizer. The semantics match corresponding OpenVINO operation classes
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declared in `namespace opset1`.
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@ -3,7 +3,7 @@
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This specification document describes `opset2` operation set supported in OpenVINO.
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Support for each particular operation from the list below depends on the capabilities available in a inference plugin
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and may vary among different hardware platforms and devices. Examples of operation instances are expressed as IR V10 xml
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snippets. Such IR is generated by the Model Optimizer. The semantics match corresponding nGraph operation classes
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snippets. Such IR is generated by the Model Optimizer. The semantics match corresponding OpenVINO operation classes
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declared in `namespace opset2`.
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@ -3,7 +3,7 @@
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This specification document describes `opset3` operation set supported in OpenVINO.
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Support for each particular operation from the list below depends on the capabilities available in a inference plugin
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and may vary among different hardware platforms and devices. Examples of operation instances are expressed as IR V10 xml
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snippets. Such IR is generated by the Model Optimizer. The semantics match corresponding nGraph operation classes
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snippets. Such IR is generated by the Model Optimizer. The semantics match corresponding OpenVINO operation classes
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declared in `namespace opset3`.
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@ -3,7 +3,7 @@
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This specification document describes `opset4` operation set supported in OpenVINO.
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Support for each particular operation from the list below depends on the capabilities available in a inference plugin
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and may vary among different hardware platforms and devices. Examples of operation instances are expressed as IR V10 xml
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snippets. Such IR is generated by the Model Optimizer. The semantics match corresponding nGraph operation classes
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snippets. Such IR is generated by the Model Optimizer. The semantics match corresponding OpenVINO operation classes
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declared in `namespace opset4`.
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@ -3,7 +3,7 @@
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This specification document describes `opset5` operation set supported in OpenVINO.
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Support for each particular operation from the list below depends on the capabilities available in a inference plugin
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and may vary among different hardware platforms and devices. Examples of operation instances are expressed as IR V10 xml
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snippets. Such IR is generated by the Model Optimizer. The semantics match corresponding nGraph operation classes
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snippets. Such IR is generated by the Model Optimizer. The semantics match corresponding OpenVINO operation classes
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declared in `namespace opset5`.
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@ -3,7 +3,7 @@
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This specification document describes `opset6` operation set supported in OpenVINO.
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Support for each particular operation from the list below depends on the capabilities available in a inference plugin
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and may vary among different hardware platforms and devices. Examples of operation instances are expressed as IR V10 xml
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snippets. Such IR is generated by the Model Optimizer. The semantics match corresponding nGraph operation classes
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snippets. Such IR is generated by the Model Optimizer. The semantics match corresponding OpenVINO operation classes
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declared in `namespace opset6`.
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@ -3,7 +3,7 @@
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This specification document describes the `opset7` operation set supported in OpenVINO™.
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Support for each particular operation from the list below depends on the capabilities available in an inference plugin
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and may vary among different hardware platforms and devices. Examples of operation instances are provided as IR V10 xml
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snippets. Such IR is generated by the Model Optimizer. The semantics match corresponding nGraph operation classes
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snippets. Such IR is generated by the Model Optimizer. The semantics match corresponding OpenVINO operation classes
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declared in `namespace opset7`.
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@ -3,7 +3,7 @@
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This specification document describes the `opset8` operation set supported in OpenVINO™.
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Support for each particular operation from the list below depends on the capabilities of an inference plugin
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and may vary among different hardware platforms and devices. Examples of operation instances are provided as IR V10 xml
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snippets. Such IR is generated by the Model Optimizer. The semantics match corresponding nGraph operation classes
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snippets. Such IR is generated by the Model Optimizer. The semantics match corresponding OpenVINO operation classes
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declared in `namespace opset8`.
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